Proceedings Paper

We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step,
we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and
texture-based image features are computed at five different scales, and a multiview boosting method is adopted to
cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize
convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict
cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling,
rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two
tissue microarrays (TMA) – TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185
tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of
0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This
demonstrates that the proposed method can potentially improve prostate cancer pathology.